A Machine-Learning Approach to Identify the Influence of Temperature on FRA Measurements

Regelii Suassuna de Andrade Ferreira, Patrick Picher, Hassan Ezzaidi and Issouf Fofana
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Regelii Suassuna de Andrade Ferreira: Research Chair on the Aging of Power Network Infrastructure (ViAHT), Department of Applied Sciences (DSA), Université du Québec à Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada
Patrick Picher: Hydro-Québec’s Research Institute (IREQ), Varennes, QC J3X 1S1, Canada
Hassan Ezzaidi: Research Chair on the Aging of Power Network Infrastructure (ViAHT), Department of Applied Sciences (DSA), Université du Québec à Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada
Issouf Fofana: Research Chair on the Aging of Power Network Infrastructure (ViAHT), Department of Applied Sciences (DSA), Université du Québec à Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada

Energies, 2021, vol. 14, issue 18, 1-14

Abstract: Frequency response analysis (FRA) is a powerful and widely used tool for condition assessment in power transformers. However, interpretation schemes are still challenging. Studies show that FRA data can be influenced by parameters other than winding deformation, including temperature. In this study, a machine-learning approach with temperature as an input attribute was used to objectively identify faults in FRA traces. To the best knowledge of the authors, this has not been reported in the literature. A single-phase transformer model was specifically designed and fabricated for use as a test object for the study. The model is unique in that it allows the non-destructive interchange of healthy and distorted winding sections and, hence, reproducible and repeatable FRA measurements. FRA measurements taken at temperatures ranging from −40 °C to 40 °C were used first to describe the impact of temperature on FRA traces and then to test the ability of the machine learning algorithms to discriminate between fault conditions and temperature variation. The results show that when temperature is not considered in the training dataset, the algorithm may misclassify healthy measurements, taken at different temperatures, as mechanical or electrical faults. However, once the influence of temperature was considered in the training set, the performance of the classifier as studied was restored. The results indicate the feasibility of using the proposed approach to prevent misclassification based on temperature changes.

Keywords: frequency response analysis interpretation; transformer condition monitoring; machine learning; comparative standard deviation; support vector machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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